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AZ-204 Develop Azure compute solutions Practice Question

You are a developer at a financial services company. You need to design a solution for processing real-time stock trade data. The system receives thousands of trades per second from an on-premises system. Each trade must be validated, enriched with reference data, and then stored in a data lake for analytics. You have the following requirements: - The processing must be serverless and scale automatically with high throughput. - The enrichment step requires calling an external REST API that can handle up to 100 requests per second. If the API is overwhelmed, trades must be retried with exponential backoff. - The solution must minimize cost and operational overhead. - Trades must be processed in order per stock symbol. You provision an Azure Event Hubs namespace with a single event hub. Trades are sent to the event hub with the stock symbol as the partition key. You configure an Azure Functions app with an Event Hubs trigger to process events. The function validates, enriches by calling the external API, and writes the enriched trade to Azure Data Lake Storage. During testing, you notice that some trades are processed out of order for the same stock symbol when the external API throttles requests. What should you do to ensure ordering per stock symbol?

⚠ Common exam trap

Test-takers frequently assume increasing batch size improves throughput without realizing that it can break ordering when retries are involved, or they mistakenly think Durable Functions are needed for any ordering requirement.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Set the 'maxEventBatchSize' to 1 in the host.json file to process one event at a time per instance.

Setting 'maxEventBatchSize' to 1 ensures that each function instance processes only one event at a time. When the external API throttles and triggers retries with exponential backoff, processing of subsequent events for the same partition (stock symbol) is blocked until the current event completes. This preserves the per-partition ordering guarantee that Event Hubs provides, as events within a partition are processed sequentially by a single consumer.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Use Durable Functions to orchestrate the processing and enforce ordering.

    Why it's wrong here

    Using Durable Functions for simple sequential processing within an Event Hubs partition introduces unnecessary architectural complexity and overhead. Event Hubs inherently guarantees message ordering *within a single partition*, meaning events sent to the same partition are delivered to consumers in the order they were published. Durable Functions are designed for stateful, long-running orchestrations and complex workflows, which are not required when the primary goal is merely to preserve the existing stream order.

  • Increase the 'maxEventBatchSize' setting to 100 in the host.json file to improve throughput.

    Why it's wrong here

    Increasing the 'maxEventBatchSize' to 100 would allow the Azure Function to receive and process up to 100 events concurrently within a single batch from an Event Hubs partition. While this can improve overall throughput by reducing trigger invocations, it directly compromises the requirement for strict ordering. Processing multiple events from the same partition simultaneously means there's no guarantee that the processing of event N will complete before event N+1, leading to out-of-order results.

  • Set the 'maxEventBatchSize' to 1 in the host.json file to process one event at a time per instance.

    Why this is correct

    Setting 'maxEventBatchSize' to 1 in the host.json file ensures that the Azure Function's Event Hubs trigger processes only one event at a time from each partition it consumes. This configuration is critical for maintaining strict sequential processing order within an Event Hubs partition. By forcing single-event processing, the function guarantees that event N is fully processed before event N+1 from the same partition is even delivered, thereby preserving the required transactional integrity.

  • Use a different partition key such as a unique trade ID to distribute load evenly.

    Why it's wrong here

    Using a different partition key, such as a unique trade ID, would indeed distribute the load more evenly across Event Hubs partitions. However, this approach would shatter the crucial ordering requirement for events related to the *same stock symbol*. Event Hubs only guarantees ordering *within a single partition*; if trades for the same stock symbol are spread across multiple partitions, their processing order cannot be guaranteed, leading to potential inconsistencies in financial calculations.

Quick reference

Cloud Service Model Comparison

ModelYou ManageProvider ManagesExamples
IaaSOS, runtime, apps, dataHardware, hypervisor, networkingEC2, Azure VMs, GCP Compute Engine
PaaSApps and dataOS, runtime, middleware, hardwareElastic Beanstalk, Azure App Service
SaaSData and settings onlyEverything elseMicrosoft 365, Salesforce, Workday
FaaS / ServerlessFunction code onlyInfra, scaling, runtimeLambda, Azure Functions, Cloud Run
CaaSContainers and appsKubernetes, OS, hardwareEKS, AKS, GKE

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